Monthly Archives: March 2017

(Excerpt from original post on the Taneja Group News Blog)

What’s a Cloud Converged system? It is really what us naive people thought hybrid storage was all about all along. Yet until now no high performance enterprise class storage ever actually delivered it. But now, Oracle’s latest ZFS Storage Appliance, the ZS5, comes natively integrated with Oracle Cloud storage. What does that mean? On-premise ZS5 Storage Object pools now extend organically into Oracle Cloud storage (which is also made up of ZS storage) – no gateway or third party software required.

Oracle has essentially brought enterprise hybrid cloud storage to market, no integration required. I’m not really surprised that Oracle has been able to roll this out, but I am a little surprised that they are leading the market in this area.

Why hasn’t Dell EMC come up with a straightforward hybrid cloud leveraging their enterprise storage and cloud solutions? Despite having all the parts, they failed to actually produce the long desired converged solution – maybe due to internal competition between infrastructure and cloud divisions? Well, guess what. Customers want to buy hybrid storage, not bundles or bunches of parts and disparate services that could be integrated (not to mention wondering who supports the resulting stack of stuff).

Some companies so married to their legacy solutions that they, like NetApp for example, don’t even offer their own cloud services – maybe they were hoping this cloud thing would just blow over? Maybe all those public cloud providers would stick with web 2.0 apps and wouldn’t compete for enterprise GB dollars?

(Microsoft does have StorSimple which may have pioneered on-prem storage integrated with cloud tiering (to Azure). However, StorSimple is not a high performance, enterprise class solution (capable of handling PBs+ with massive memory accelerated performance). And it appears that Microsoft is no longer driving direct sales of StorSimple, apparently positioning it now only as one of many on-ramps to herd SME’s fully into Azure.)

We’ve reported on the Oracle ZFS Storage Appliance itself before. It has been highly augmented over the years. The Oracle ZFS Storage Appliance is a great filer on its own, competing favorably on price and performance with all the major NAS vendors. And it provides extra value with all the Oracle Database co-engineering poured into it. And now that it’s inherently cloud enabled, we think for some folks it’s likely the last storage NAS they will ever need to invest in (if you’ll want more performance, you will likely move to in-memory solutions, and if you want more capacity – well that’s what the cloud is for!).

Oracle’s Public Cloud is made up of – actually built out of – Oracle ZFS Storage Appliances. That means the same storage is running on the customer’s premise as in the public cloud they are connected with. Not only does this eliminate a whole raft of potential issues, but solving any problems that might arise is going to be much simpler – (and less likely to happen given the scale of Oracle’s own deployment of their own hardware first).

Compare this to NetApp’s offering to run a virtual image of NetApp storage in a public cloud that only layers up complexity and potential failure points. We don’t see many taking the risk of running or migrating production data into that kind of storage. Their NPS co-located private cloud storage is perhaps a better offering, but the customer still owns and operates all the storage – there is really no public cloud storage benefit like elasticity or utility pricing.

Other public clouds and on-prem storage can certainly be linked with products like Attunity CloudBeam, or additional cloud gateways or replication solutions. But these complications are exactly what Oracle’s new offering does away with.

There is certainly a core vendor alignment of on-premises Oracle storage with an Oracle Cloud subscription, and no room for cross-cloud brokering at this point. But a ZFS Storage Appliance presents no more technical lock-in than any other NAS (other than the claim that they are more performant at less cost, especially for key workloads that run Oracle Database.), nor does Oracle Cloud restrict the client to just Oracle on-premise storage.

And if you are buying into the Oracle ZFS family, you will probably find that the co-engineering benefits with Oracle Database (and Oracle Cloud) makes the set of them all that much more attractive (technically and financially). I haven’t done recent pricing in this area, but I think we’d find that while there may be cheaper cloud storage prices per vanilla GB out there, looking at the full TCO for an enterprise GB, hybrid features and agility could bring Oracle Cloud Converged Storage to the top of the list.

An IT industry analyst article published by SearchITOperations.

Securing IoT data should become a priority as more companies manipulate the volumes produced by these devices. Seemingly innocuous information could allow privacy invasions.

Mike Matchett

The data privacy and access discussion gets all the more complicated in the age of IoT.

Some organizations might soon suffer from data paucity — getting locked, outbid or otherwise shut out of critical new data sources that could help optimize future business. While I believe that every data-driven organization should start planning today to avoid ending up data poor, this concern is just one of many potential data-related problems arising in our new big data, streaming, internet of things (IoT) world. In fact, issues with getting the right data will become so critical that I predict a new strategic data enablement discipline will emerge to not just manage and protect valuable data, but to ensure access to all the necessary — and valid — data the corporation might need to remain competitive.

In addition to avoiding debilitating data paucity, data enablement will mean IT will also need to consider how to manage and address key issues in internet of things data security, privacy and veracity. Deep discussions about the proper use of data in this era of analytics are filling books, and much remains undetermined. But IT needs to prepare for whatever data policies emerge in the next few years.

Piracy or privacy?

Many folks explore data privacy in depth, and I certainly don’t have immediate advice on how to best balance the personal, organizational or social benefits of data sharing, or where to draw a hard line on public versus private data. But if we look at privacy from the perspective of most organizations, the first requirements are to meet data security demands, specifically the regulatory and compliance laws defining the control of personal data. These would include medical history, salary and other HR data. Many commercial organizations, however, reserve the right to access, manage, use and share anything that winds up in their systems unless specifically protected — including any data stored or created by or about their employees.

If you are in the shipping business, using GPS and other sensor data from packages and trucks seems like fair game. After all, truck drivers know their employers are monitoring their progress and driving habits. But what happens when organizations track our interactions with IoT devices? Privacy concerns arise, and the threat of an internet of things security breach looms.

Many people are working hard to make GPS work within buildings, ostensibly as a public service, using Wi-Fi equipment and other devices to help triangulate the position of handheld devices and thus locate people in real time, all the time, on detailed blueprints.

In a shopping mall, this tracking detail would enable directed advertising and timely deals related to the store a shopper enters. Such data in a business setting could tell your employer who is next to whom and for how long, what you are looking at online, what calls you receive and so on. Should our casual friendships — not to mention casual flirting — bathroom breaks and vending machine selections be monitored this way? Yet the business can make the case that it should be able to analyze those associations in the event of a security breach — or adjust health plan rates if you have that candy bar. And once that data exists, it can be leaked or stolen…(read the complete as-published article there)

An IT industry analyst article published by SearchSQLServer.

Artificial intelligence is a hot topic in IT, and Microsoft has made strides to synchronize SQL Server with machine learning tools for use in analyzing operational data pipelines.

Mike Matchett

One of the hottest IT trends today is augmenting traditional business applications with artificial intelligence or machine learning capabilities. I predict the next generation of data center application platforms will natively support the real-time convergence of online transaction processing with analytics. Why not bring the point of the sword on operational insight to the frontline where business actually happens?

But modifying production application code that is optimized for handling transactions to embed machine learning algorithms is a tough slog. As most IT folks are reluctant — OK, absolutely refuse — to take apart successfully deployed operational applications to fundamentally rebuild them from the inside out, software vendors have rolled out some new ways to insert machine intelligence into business workflows. Microsoft is among them, pushing SQL Server machine learning tools tied to its database software.

Basically, adding intelligence to an application means folding in a machine learning model to recognize patterns in data, automatically label or categorize new information, recommend priorities for action, score business opportunities or make behavioral predictions about customers. Sometimes this intelligence is overtly presented to the end user, but it can also transparently supplement existing application functionality.

In conventional data science and analytics activities, machine learning models typically are built, trained and run in separate analytics systems. But models applied to transactional workflows require a method that enables them to be used operationally at the right time and place, and may need another operational method to support ongoing training (e.g., to learn about new data).
Closeness counts in machine learning

In the broader IT world, many organizations are excited by serverless computing and lambda function cloud services in which small bits of code are executed in response to data flows and event triggers. But this isn’t really a new idea in the database world, where stored procedures have been around for decades. They effectively bring compute processes closer to data, the core idea behind much of today’s big data tools.

Database stored procedures offload data-intensive modeling tasks such as training, but can also integrate machine learning functionality directly into application data flows. With such injections, some transactional applications may be able to take advantage of embedded intelligence without any upstream application code which needs to be modified. Additionally, applying machine learning models close to the data in a database allows the operational intelligence to be readily shared among different downstream users…(read the complete as-published article there)

RT @TruthinIT: There's no cost of goods like a traditional NAS device where I've got disks I've got to pay for. And if I'm not using the data on those disks, I still got to pay for those disks. bit.ly/2BBX073@Nasuni@smworldbigdata

In 30 min I'm interviewing @Cohesity (and customer) on @TruthinIT about Mass Data Fragmentation. It's about having too many copies in about four or five different "dimensions", including cloud! Join us webcast (12.11.18) @ 1pmET (and there will be prizes) bit.ly/2PdqrQn